Implementing machine learning algorithms to optimize sprint performance and biomechanical analysis of track and field athletes

Sprint performance is a crucial component of athletic performance, especially in sports like track and field, football, and rugby, which require quick bursts of peak effort over short durations. Understanding the biomechanics of sprinting is essential for enhancing athletic performance, preventing injuries, and creating effective training plans. Traditional research on sprint evaluation often focuses on discrete measures while neglecting the intricate interactions between variables that evolve throughout the sprint. This study addresses these challenges by applying a machine learning (ML) algorithm, specifically the Polar Bear-tuned Multi-Source Kernel Support Vector Machine (PB-MKSVM), to predict and optimize the sprint performance of track and field athletes. The system analyzes essential biomechanical characteristics such as muscle activation patterns, joint angles, ground reaction forces, and stride length. Data were collected using wearable sensors and motion capture systems during standardized sprint trials, during which various biomechanical parameters were recorded. Standard preprocessing steps including noise removal and outlier detection were applied to the data. Power Spectral Density (PSD) was employed to extract features from the preprocessed data. The results demonstrate that the proposed method outperforms traditional algorithms in predicting sprinting efficiency and identifies complex, phase-specific changes in movement patterns. The model effectively analyzes the intricate biomechanics of sprinters’ movements to differentiate between various skill levels. Using Python software, the model achieved impressive performance metrics, including accuracy (94.5%), precision (92.7%), recall (93.6%), F1-score (92.1%), R 2 (0.92), and AUC (0.91), highlighting its robust predictive ability. This study illustrates how machine learning models can advance research in sprinting mechanics and provide insightful information to athletes and coaches seeking to improve performance.